Today's mobile networks serve a high number of users, equipped e.g. with smartphones, tablets and notebooks. In addition, more and more machines and Internet of things (IoT) also transmit and receive data through wireless networks. The result is a very diverse traffic in the network: it ranges from data transfers with high volume that last for a relatively long time span (e.g. a file download that takes several minutes) to very short transmissions (e.g. a text message of 140 characters). This mix of different data packet streams is transported through one common mobile network.
The mobile network (e.g. a Long Term Evolution (LTE) network) operates in a certain frequency band for which the operator of the network holds a license. An important target is to transmit the data in this frequency band in a very effective manner such that the amount of data transmitted and received is maximized. A measure for this effectiveness is the so-called spectral efficiency. It describes how many data bits a transmitter can transfer to the receiver in a certain time and on a certain frequency band. The maximization of the spectral efficiency is particularly achieved by an adaptive modulation and coding. Depending on the signal-to-interference-and-noise ratio (SINR) the receiver experiences, the transmitter adapts certain parameters of the transmission-particularly, the modulation scheme, the coding rate and the multiple-input multiple-output (MIMO) scheme. The higher the SINR, the more bits a transmission can transfer in a certain timeframe on a certain frequency band. However, this principle implies a risk: in case the transmitter does not estimate the SINR at the receiver correctly, it might select inappropriate transmission parameters. This can result in an under- or overestimation of the SINR. In the first case, the transmission is not as efficient as it could be, i.e. less data is transmitted than it would actually be possible. The latter case is more problematic: due to higher interference at the receiver, the transmitted data cannot be decoded at the receiver. The data is either lost or has to be retransmitted.
Both problems limit the spectral efficiency of the system. The situation is especially problematic in the presence of so-called bursty traffic. Bursty traffic originates from short transmissions as illustrated in the following example:
Mobile station (MS) 1 is attached to a base station (BS) 1. MS 1 downloads a large file and results in a relatively long duration of data transmission from BS 1 to MS 1. To make this data transmission as efficient as possible, BS 1 constantly estimates the current SINR at MS 1 and adapts the transmission parameters accordingly. However, the estimation always follows a certain delay, such that BS 1 is never aware of the real instantaneous SINR at MS 1. In case a BS 2 starts a data transmission to an MS 2 in the same frequency band, this will interfere with MS 1 and reduce the SINR at MS 1. This may then cause decoding errors in the receiver of MS 1 and thus data loss. After a certain time, BS 1 has adapted to the new situation e.g. by lowering the modulation scheme. In case BS 2 stops its transmission, BS 1 again needs a certain time to adapt to the improved situation. The most problematic case is caused by a constant on/off behavior of BS 2. In that case, BS 1 can never adapt to the correct situation. A constant on/off behavior can especially be caused by services with a relatively low data rate and small transmissions from time to time e.g. chat-services, downloads of web pages with many different elements that are transmitted independently.
The problems caused by bursty traffic have already been pointed out previously. There are also existing solutions available in the market, which will be described as below.
Solution 1: Adaptation of Transmission Parameters and Machine Learning
It is possible to limit the effects of bursty traffic by adapting transmission parameters in advance. It is for example possible to use a lower modulation scheme than the currently estimated best one in order to avoid decoding problems due to unexpected interference. However, this will reduce the spectral efficiency. In addition it is possible to enhance this principle by machine learning techniques. In case an interferer has a predictable behavior, a future transmission of this interferer can be taken into account. A drawback is the complexity of such methods and the probability of erroneous predictions.
Solution 2: Coordination
Coordination between BSs is a solution for the problems described above. In a downlink direction (for traffic from a BS to an MS) a BS can inform neighboring BSs about simultaneous transmissions that cause interference. As a result a BS can take into account such interference when selecting the transmission parameters. Also in an uplink direction (for traffic from an MS to a BS) this principle can be applied, e.g. such that BS 1 receives information from BS 2 about ongoing uplink transmissions and BS 1 forwards this information to its attached MSs. Coordination can also happen through a central controller.
However, there are certain drawbacks of such coordination. The information exchange between the BSs is typically subjected to delays. This is the case, as connections between BSs (e.g. through fiber optics) often do not follow the direct path but undergo switching and/or routing. The delay might then again cause the BSs to be unaware of the real-time situation in other BSs. Consequently, this results in implementation effort and cost for the required hard- and software.
Examples for existing coordination techniques are cited in    [1] Ahmed, M. H., Yanikomeroglu, H., Mahmoud, S., & Falconer, D. (2002). Scheduling of multimedia traffic in interference-limited broadband wireless access networks. In The 5th International Symposium on Wireless Personal Multimedia Communications (Vol. 3, pp. 1108-1112). IEEE, and http://doi.org/10.1109/WPMC.2002.1088350    [2] US2012/0027108